Next Article in Journal
A Study on the Thermomechanical Reliability Risks of Through-Silicon-Vias in Sensor Applications
Previous Article in Journal
Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(2), 314; doi:10.3390/s17020314

Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm

1
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
2
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 8 December 2016 / Revised: 24 January 2017 / Accepted: 4 February 2017 / Published: 8 February 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [1081 KB, uploaded 15 February 2017]   |  

Abstract

Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. However, current sparse representation based classification models have not considered all the test pixels simultaneously. In this paper, we propose a hyperspectral classification method with spatial filtering and \(l_{(2,1)}\) norm (SFL) that can deal with all the test pixels simultaneously. The \(l_{(2,1)}\) norm regularization is used to extract relevant training samples among the whole training data set with joint sparsity. In addition, the \(l_{(2,1)}\) norm loss function is adopted to make it robust for samples that deviate significantly from the rest of the samples. Moreover, to take the spatial information into consideration, a spatial filtering step is implemented where all the training and testing samples are spatially averaged with its nearest neighbors. Furthermore, the non-negative constraint is added to the sparse representation matrix motivated by hyperspectral unmixing. Finally, the alternating direction method of multipliers is used to solve SFL. Experiments on real hyperspectral images demonstrate that the proposed SFL method can obtain better classification performance than some other popular classifiers. View Full-Text
Keywords: alternating direction method of multipliers; hyperspectral classification; outliers; spatial filtering and \(l_{(2,1)}\) norm (SFL) alternating direction method of multipliers; hyperspectral classification; outliers; spatial filtering and \(l_{(2,1)}\) norm (SFL)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, H.; Li, C.; Zhang, C.; Liu, Z.; Liu, C. Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm. Sensors 2017, 17, 314.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top